Autonomous vehicles typically utilize multiple data sources to determine their location, to identify other vehicles, to identify potential hazards, and to develop navigational routing strategies. These data sources can include a central map database that is preloaded with road locations and traffic rules corresponding to areas on the map. Data sources can also include a variety of sensors on the vehicle itself to provide real-time information relating to road conditions, other vehicles and transient hazards of the type not typically included on a central map database.
In many instances a mismatch can occur between the map information and the real-time information sensed by the vehicle. Various strategies have been proposed for dealing with such a mismatch. For example, U.S. Pat. No. 8,718,861 to Montemerlo et al. teaches detecting deviations between a detailed map and sensor data and alerting the driver to take manual control of the vehicle when the deviations exceed a threshold. U.S. Pub. No. 2014/0297093 to Murai et al. discloses a method of correcting an estimated position of the vehicle by detecting an error in the estimated position, in particular when a perceived mismatch exists between road location information from a map database and from vehicle sensors, and making adjustments to the estimated position.
A variety of data sources can be used for the central map database. For example, the Waze application provides navigational mapping for vehicles. Such navigational maps include transient information about travel conditions and hazards uploaded by individual users. Such maps can also extract location and speed information from computing devices located within the vehicle, such as a smart phone, and assess traffic congestion by comparing the speed of various vehicles to the posted speed limit for a designated section of roadway.
Strategies have also been proposed in which the autonomous vehicle will identify hazardous zones relative to other vehicles, such as blind spots. For example, U.S. Pat. No. 8,874,267 to Dolgov et al. discloses such a system. Strategies have also been developed for dealing with areas that are not detectable by the sensors on the vehicle. For example, the area behind a large truck will be mostly invisible to the sensors on an autonomous vehicle. U.S. Pat. No. 8,589,014 to Fairfield et al. teaches a method of calculating the size and shape of an area of sensor diminution caused by an obstruction and developing a new sensor field to adapt to the diminution.
Navigational strategies for autonomous vehicles typically include both a destination-based strategy and a position-based strategy. Destination strategies involve how to get from point ‘A’ to point ‘B’ on a map using known road location and travel rules. These involve determining a turn-by-turn path to direct the vehicle to the intended destination. Position strategies involve determining optimal locations for the vehicle (or alternatively, locations to avoid) relative to the road surface and to other vehicles. Changes to these strategies are generally made during the operation of the autonomous vehicle in response to changing circumstances, such as changes in the position of surrounding vehicles or changing traffic conditions that trigger a macro-level rerouting evaluation by the autonomous vehicle.
Position-based strategies have been developed that automatically detect key behaviors of surrounding vehicles. For example, U.S. Pat. No. 8,935,034 to Zhu et al. discloses a method for detecting when a surrounding vehicle has performed one of several pre-defined actions and altering the vehicle control strategy based on that action.
One of many challenges for controlling autonomous vehicles is managing interactions between autonomous vehicles and human-controlled vehicles in situations that are often handled by customs that are not easily translated into specific driving rules.